[center]Published 11/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.94 GB | Duration: 4h 31m

Reliability, Availability & Mantainability analysis on electrical power systems using Monte Carlo simulations and MATLAB[/center]

What you'll learn
Learn reliability, availability and mantainaility fundamental concepts
Learn how to detect the failing modes responsible for having a complete system shutdown
Build individual components failure and reparing time period modeling, from historical data and/or other references using random number generation
Learn to model a whole system's failures and reparing times from its individual building blocs modeling
Learn to estimate realiability, availability and mantainability of a system using Matlab and monte carlo
Create software capable of simulating any kind of system from scratch
Requirements
Basic programming knowledge using Matlab (or Octave)
Basic engineering knowledge
Basic probability and statistics knowledge
Description
Reliability centered maintenance has become a common practice on maintenance departments over all kinds of fields, from pretoleum fields to power system, passing through naval and aeronautical industry. Reliability centered maintenance allows for a cost effective maintenance policy that is focused on a system's different modes of failure and consecuences.Most of the times, the failures of a system are fundamentaly random in their behaviour, thus, having a tool capable of simulating this random behaviour thousands or even millions of times in order to get a statistical trend is extemely valuable so that we can plan maintenance policies that tackle the most likely failing modes and the most catastrophic ones.Monte Carlo methods is an umbrella terms that covers all the studies that rely on many similations of random systems in order to get their most likely behaviour over the span of several tries. I had the opportunity to work this specific topic on my undergraduate thesis ''RAM analysis of electrical power system on the operational context using sequential Monte Carlo'' back in 2016 and got awarded with honors upon my disertation. I'll thrive myself to pour everything I learned into this course. I'm looking forward for your questions and feedback!
Overview
Section 1: Introduction
Lecture 1 Course scope and objectives
Section 2: Basic concepts
Lecture 2 Why Monte Carlo?
Lecture 3 Mathematical concepts: Probability density function (PDF).
Lecture 4 Mathematical concepts: CPF and ICPF.
Lecture 5 Practical examples: Random sample generation through ICPFs
Lecture 6 Mathematical concepts: STD, SEM and CI.
Lecture 7 Practical examples: Commands for acquiring STD, SEM and CI.
Lecture 8 Reliability, Availability and Mantainability definitions (TTF, TTR ,MTTF, MTTR)
Lecture 9 Practical examples: Adjusting a sample to a probability density function
Lecture 10 Simulating an individual component's on and off status switches over tmission 1
Lecture 11 Simulating an individual component's on and off status switches over tmission 2
Lecture 12 Creating our first Monte Carlo RAM analysis code for an individual component
Lecture 13 How many simulations are enough?
Lecture 14 Exponential probability density function (''memoryless'' function)
Section 3: How do systems fail?
Lecture 15 What is a reliability block diagram?
Lecture 16 Simulating series and parallel RBDs
Lecture 17 Minimum cut sets (applied to electrical power systems) for RBD construction.
Lecture 18 The adjecency matrix of a graph
Lecture 19 Generating the adjacency matrix of a power system using Matlab
Lecture 20 Practical case: applying the adjacency matrix function to our big case of study.
Lecture 21 Example of elaboration of an RBD for a power system.
Lecture 22 How do systems fail and how to simulate this failings?
Section 4: Building our Monte Carlo RAM analysis software
Lecture 23 Simulating a series system
Lecture 24 Simulating a parallel system
Lecture 25 Making sure our Monte Carlo RAM Analysis software works
Lecture 26 Finding the weak links within the RBD
Lecture 27 Final project of section 4 (part I)
Lecture 28 Final project of section 4 (part II)
Section 5: LOLP and LOLE
Lecture 29 Loss of load probability, Loss of load expectation and how to calculate them.
Lecture 30 Applying LOLP and LOLE to build a deterministic MATLAB simulation
Lecture 31 Why Monte Carlo? Part I
Lecture 32 Why Monte Carlo? Part II
Lecture 33 Explaining a matlab function to calculate LOLE and LOLP
Lecture 34 Creating a Monte Carlo software to calculate LOLP and LOLE
Lecture 35 LOLE and LOLP as a function of the target load
Lecture 36 Evolving LOLP and LOLE for a big generation system
Lecture 37 Wrapping up LOLE and LOLP
Section 6: Wrapping up all
Lecture 38 Wrapping up all
Section 7: Congratulations and course closing
Lecture 39 bye bye
Lecture 40 BONUS
Engineers and engineering students,Matlab practitioners,Reliability centered maintenance engineers,Reliability engineers,Maintenance engineers,Maintenance managers

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